On Certain Algebraic Properties of Symbolic 3-Plithogenic Real Square Matrices

P.; Bilal; Turayeva Dinara

doi:https://doi.org/10.54216/IJNS.230427

Financial Technology and Innovation | 2024

Regime-Aware Digital-Asset Allocation: Balancing Participation and Downside Risk

The exposure of cryptocurrencies has been so easy to make a part of the digital-asset platforms, but the risk engines that come with these products are frequently based on static allocations or unconditional correlation estimates. The problem is that the states that allow participation in upside potential are different from the states that allow protection against sudden drawdowns; solving both optimization problems as a single problem can lead to a portfolio that retains too much crypto risk or to a portfolio with a zero digital asset exposure. In this paper, we introduce a participation constrained, regime aware allocation framework which allocates Bitcoin and Ether alongside gold and the S&P 500. A pre-specified volatility signal separates out the normal period from the stress period, and normal period minimum variance optimization enforces a significant crypto allocation during the normal period, whereas the stress period has an explicit ceiling on the amount of crypto invested. The design is tested without look-ahead bias over 2020–2023, assuming that there is a 10 basis points per unit of turnover charge for the design. Importance of the framework for FinTech investment platforms is that it converts a qualitative risk setting into allocation rules that are easily explainable, auditable, and repeatable. The volatility of the static crypto portfolio was 64.7%, the maximum drawdown was 72.1%, and the conditional loss at 5% monthly was 34.6% for the portfolio. A crypto-only strategy had an annualized return of 12.0% and reduced volatility to 16.3% and the 5% conditional loss to 20.1%, while a regime-aware strategy had an annualized return of 14.9% and reduced volatility to 25.9% and the 5% conditional loss to 14.5%.

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Andino Maseleno mail -

Aa Hubur mail

link https://doi.org/10.54216/FinTech-I.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2024

Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing

Wireless Sensor Networks (WSN) play a crucial role in diverse data gathering applications, but face a significant challenge in the form of limited energy reserves within sensor nodes. Enhancing the network's Quality of Service, particularly its lifetime, is paramount. Prolonging the network's operational span hinges on mitigating energy consumption, with communication accounting for a substantial portion of nodal power usage. By reducing data transmission, not only can energy consumption be curtailed, but also bandwidth requirements and network congestion can be minimized. In the context of Wireless Sensor Networks, the Distributed Similarity-based Clustering and Compressed Forwarding (DSCCF) approach strives to construct data-similar iso-clusters with minimal communication overhead. This technique involves extracting trend and magnitude components from lengthy data series using an LMS filter, resulting in what is termed "data projection." Data similarity between nodes is assessed by measuring the Euclidean distance between these data projections, thereby facilitating efficient and low-overhead iso-cluster formation. To further economize intra-cluster communication, an adaptive-nLMS-based dual prediction framework is employed. During each data collection round, the cluster head holds instantaneous data for each cluster member, using either prediction or direct data communication. Furthermore, inter-cluster data is reduced via a multi-level lossless compressive forwarding technique. Impressively, this proposed approach has achieved an 80% reduction in data while maintaining optimal data accuracy for the collected information. The transmission of inter-cluster data exclusively occurs through a network backbone comprised solely of cluster heads. Initially, the cluster heads establish this network backbone. Each cluster head dispatches a link request query towards the sink through the backbone, receiving a link reply message containing path length and the weakest link of the path. The cluster head repeats this process for each available path, subsequently selecting the most optimal path based on the acquired information and its reliability in terms of link quality

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Jayasudha A. R. mail -

Ramya S. mail -

Vairaprakash S. mail -

N. Kannaiya Raja mail

link https://doi.org/10.54216/IJWAC.080201

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2024

Multisensory Fusion Approaches for Accurate Smoke Detection in Smart Environments

The reassessment of alarm systems’ role in this regard has led to the search for improved ways of detecting fire. In this study, sensor fusion is explored to improve the accuracy and reliability of smoke detection. Since individual sensors are limited in their capabilities, this research seeks to merge different sensor data using complex fusion techniques. This paper gives a detailed analysis of several types of sensors that are used indoors and outdoors as well as firefighter training grounds that have multiple fire sources. To work around this problem, the Adaboost algorithm was used as an ensemble learning technique where sensor data were combined iteratively to form a strong classification model. The study then goes on to meticulously plot variable distribution graphs/bar charts, carry out correlation analyses, and make comparisons with other studies done previously; these findings give insight into how effective sensor fusion methods could be when it comes to smoke detection. The research results indicate that incorporating multiple sensors can significantly enhance detection accuracy and reliability. Thus, the findings obtained from this study identify a promising path for creating more efficient smoke detection systems.

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Ahmed Hatip mail -

Karla Zayood mail

link https://doi.org/10.54216/IJWAC.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2026

Using Mathematical Modeling to Predict the Spread of Epidemics

In this paper, we give a brief overview of the advances in mathematical modelling applied to epidemiology and the achievements that have been made in epidemiological research using mathematical models. Since the early 1900s when the well-known susceptible–infected–recovered model was proposed, mathematical treatments have played an important role in epidemiology. We introduce the deterministic SIR model and explain the value of computational tools, mathematical modelling and statistical analysis for the understanding of the development of disease and the size of emerging outbreaks. These are issues that are of major concern in everyday life and public health. With the emergence of the novel coronavirus in recent times, it has become evident that mathematical modeling along with computational and statistical tools are extremely relevant. Hence, this study explains the mathematical modelling in Epidemiology and demonstrates the advancement of the discipline over the years.

groups

Nabaa Fawze mail

link https://doi.org/10.54216/GJMSA.130202

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Revolutionizing Data Management through Cloud-Based Data Fusion Platforms in Distributed Network Architectures

Data management is developing rapidly, and we need solutions that can handle massive volumes of diverse data. Especially for cloud-based data fusion and global network designs. Our research offers a fresh solution. Each difficult formula in this manner improves the system. Standardizing, matching, translating, and merging data from several sources is the fundamental strategy for data integration and management. We found that this alternative is superior to standard data management systems for growing, working fast, consistently, securely, and accurately integrating data, as well as cost-effectiveness. Data's visual presentation enhances the method's advantages and shows its potential. This research proves the technique works and illustrates how it may be utilized to advance the field. Supporting today's sophisticated data systems is a major advance. It's a solid, scalable data management solution that can evolve.

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Bambang Sujatmiko mail -

Mohammad Ahmar Khan mail -

Ved Prakash Mishra mail -

Bondili N. Sai Bhavya Charitha mail -

Dattatraya Subhash Jadhav mail -

Prerak Sudan mail

link https://doi.org/10.54216/FPA.150119

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Fusion of Expert Judgment using the Neutrosophic Delphi Method to Evaluate Tax Behavior

The tax behavior of taxpayers has gained increasing relevance in the economic and legal context of Ecuador. The tax system plays a fundamental role in the generation of income for the State and the financing of public policies. In this sense, understanding and evaluating how taxpayers comply with their tax obligations becomes crucial to guarantee equity and efficiency in tax collection. In this regard, the objective is to examine the various legal aspects that affect the compliance of tax obligations by taxpayers in the Babahoyo canton. The Neutrosophic Delphi method was used to model the study. As a result, it is seen that the tax authorities should promote educational campaigns in the canton for the knowledge and understanding of their citizens about the current regulations and thus strengthen the tax system for the benefit of the economic and social development of the country.

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Ingrid Joselyne D. Basurto mail -

Carmen Marina M. Cabrita mail -

Pico P. Ermerly Espinosa mail -

Tonguc Cagin mail

link https://doi.org/10.54216/IJNS.230423

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Fuzzy Parameterized Single-Valued Neutrosophic Subset based Artificial Intelligence for Sustainable Financial Crisis Prediction and Green Finance

Predicting sustainable financial crises and promoting green finance are paramount in fast developing economic landscape. Leveraging advanced AI-driven technologies, such as Neutrosophic logic, enables a nuanced understanding of complex sustainability factors influencing financial markets. By incorporating these advanced technologies, organizations can proactively mitigate and identify risks related to unsustainable practices while fostering investment aligned with environmental, social, and governance (ESG) principles. This proactive stance improves financial resilience and contributes to the transition towards a resilient and more sustainable financial ecosystem. We can navigate future challenges with foresight and responsibility through the synergy of sustainable financial crisis prediction and green finance initiatives, which ensures a prosperous and environmentally conscious financial future for the generation to come. This study develops a new optimal Fuzzy Parameterized Single-Valued Neutrosophic Subset for financial crisis prediction and green finance (OFPSVNS-FCPGF) technique. The OFPSVNS-FCPGF technique intends to recognize the presence of the financial disaster in the sustainable and green finance sector. In the OFPSVNS-FCPGF technique, Z-score normalization is primarily used to measure the economic data into a beneficial layout. For the procedure of prediction, the OFPSVNS-FCPGF approach designs the FPSVNS approach which detects the occurrence of financial crises or not. Furthermore, the parameter tuning of the FPSVNS technique takes place utilizing the grasshopper optimization algorithm (GOA). To illustrate the improved FCP outcomes of the OFPSVNS-FCPGF model, a series of simulations were involved. An wide comparison study specified that the OFPSVNS-FCPGF method gains significant outcomes in the green finance sector.

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Adam Mohamed Omer mail -

Fadoua Kouki mail -

Adeeb Alhebri mail -

Radwan Hussien Alkebssi mail -

Ebrahim Mohammed Al-Matari mail

link https://doi.org/10.54216/IJNS.230424

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Neutrosophic Fuzzy Simple Additive Weighting with Artificial Intelligence for Sustainable Heart Disease Recognition and Classification

Heart disease (HD) is considered the main cause of death rate around the world. Multiple systems and biomedical instruments in hospitals take large amounts of medical data. Thus, understanding the data linked with HD is vital to enhance the prediction performance. The timely intervention of HD is the most important factor in preventing patients from additional damage. In recent times, non-invasive medical procedures, including artificial intelligence-based approaches have been used in the healthcare sector. Particularly machine learning (ML) applies various techniques and algorithms that are extensively applied and are especially effective in accurately detecting HDs within short period. However, HD prediction is a challenging task. The largest size of medicinal database has made it a challenge for clinicians to understand the complicated feature relations and make disease predictions. Therefore, this study presents a Neutrosophic Fuzzy SAW with Artificial Intelligence for Sustainable Heart Disease Recognition and Classification (NFSAW-AISHDC) technique in Healthcare Sector. The NFSAW-AISHDC technique mainly focuses on the adoption of neutrosophic fuzzy simple additive weighting (NFSAW) with feature selection process for HD detection. The NFSAW-AISHDC method exploits min-max scalar to scale the input medical data. For feature selection, the NFSAW-AISHDC method uses beluga whale optimization (BWO) algorithm to choose feature subsets. Moreover, the NFSAW-AISHDC technique applies NFSAW approach to the identification of HDs. The performance values of the NFSAW-AISHDC methodology undergoes using benchmark database. The experimental outcome underlined the promising results of the NFSAW-AISHDC method with other models.

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Ahmedia Musa M. Ibrahim mail -

Mohammed M. A. Almazah mail -

Badr Eldeen A. A. Abouzeed mail -

Murtada K. Abdalla Abdelmahmod mail

link https://doi.org/10.54216/IJNS.230425

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Safeguarding Financial Integrity with Interval-Valued Neutrosophic Analytic Hierarchy Process for Sustainable Accounting Systems

Nowadays, financial integrity within sustainable accounting systems is critical endeavor in ensuring intricate landscape of sustainable finance. Detection of financial fraud within sustainable accounting systems is crucial for upholding environmental, social, and governance (ESG) standards and sustaining the integrity of financial practices. Leveraging advanced AI-driven technologies, these systems can effectively analyze abundance of financial data to detect suspicious patterns and anomalies indicative of fraudulent activities. Incorporating Neutrosophic logic into sustainable accounting systems improves the efficiency of financial fraud detection by accommodating inherent uncertainty in complex financial data. By leveraging this ground-breaking technology, organizations can effectively navigate the complex financial landscape while ensuring the integrity of their accounting practices. Neutrosophic logic facilitates the modelling of contradictory and ambiguous information, enabling more nuanced detection and analysis of fraudulent activities that may remain unnoticed. This study develops an automated financial fraud detection using improved sparrow search algorithm with Interval-Valued Neutrosophic Analytic Hierarchy Process (ISSA-IVNAHP) technique. The ISSA-IVNAHP technique aims to protect financial integrity via the identification of financial frauds in Sustainable Accounting Systems. The ISSA-IVNAHP technique incorporates a two-stage process. Initially, the ISSA-IVNAHP method designs ISSA-based feature subset selection approach for the optimal feature selection. Next, in the second stage, the ISSA-IVNAHP technique uses IVNAHP technique for decision-making process that enables to detection of the presence and absence of financial fraud. The simulation results of the ISSA-IVNAHP technique can be examined on financial fraud database. The experimental values reported that the ISSA-IVNAHP methodology attains maximum effeciency over other models

groups

Adeeb Alhebri mail

link https://doi.org/10.54216/IJNS.230426

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024